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arxiv: 2605.23454 · v1 · pith:SLS3LWPMnew · submitted 2026-05-22 · 💻 cs.CL

ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning

Xiaoyuan Li , Keqin Bao , Moxin Li , Yubo Ma , Yichang Zhang , Wenjie Wang , Fuli Feng , Dayiheng Liu This is my paper

Pith reviewed 2026-05-25 04:29 UTC · model grok-4.3

classification 💻 cs.CL
keywords ARESrubric-based RLLLM reinforcement learningautomated rubric synthesisopen-ended tasksquestion-specific rubricsscalabilityreinforcement learning from human feedback
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The pith

ARES automates the synthesis of question-specific rubrics to enable scalable rubric-based reinforcement learning for large language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ARES as a method to automatically build rubric-based training data for RL on LLMs starting from raw pretraining documents. It generates self-contained question-answer pairs along with weighted rubrics tailored to each question, conditioned on domain and persona information. Validation filters ensure quality in self-containment, faithfulness, and rubric validity. This produces 100K instances across ten domains, and RL training with these rubrics beats continual pretraining, supervised fine-tuning, and binary-reward RL on seven benchmarks, especially for open-ended multi-dimensional tasks.

Core claim

ARES converts source knowledge from pretraining documents into question-answer pairs and co-generates question-specific weighted rubrics, enabling instance-level reward supervision for open-ended responses without relying on expert-written rubrics or fixed task-level evaluations.

What carries the argument

The ARES pipeline that conditions rubric generation on domain labels and persona information while applying filters for question self-containment, answer faithfulness, and rubric validity.

If this is right

  • Constructs 100K rubric-annotated instances across ten domains from raw pretraining data.
  • Rubric-based RL with ARES outperforms continual pretraining, supervised fine-tuning, and binary-reward RL on seven benchmarks.
  • Largest performance gains occur on multi-dimensional open-ended tasks such as healthcare and instruction following.
  • Instance-level rubrics capture evaluation requirements better than fixed task-level rubrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Rubric automation could extend to other alignment techniques beyond RL by providing fine-grained feedback signals.
  • If the method generalizes, it might allow training on diverse open-ended tasks without proportional increases in human annotation effort.
  • Potential extension to dynamically updating rubrics during training based on model progress.

Load-bearing premise

The automatically generated rubrics and validation filters produce rewards that genuinely improve model behavior rather than merely rewarding outputs that match the generation process itself.

What would settle it

An experiment showing that models trained via ARES rubric rewards perform no better than or worse than those using binary rewards on held-out open-ended benchmarks.

Figures

Figures reproduced from arXiv: 2605.23454 by Dayiheng Liu, Fuli Feng, Keqin Bao, Moxin Li, Wenjie Wang, Xiaoyuan Li, Yichang Zhang, Yubo Ma.

Figure 1
Figure 1. Figure 1: Overview of the six-stage ARES pipeline. Starting from raw pretraining documents, ARES [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: UMAP visualization of questions for RaR and ARES by Qwen3-Embeddings-0.6B. RaR ARES [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Per-benchmark comparison between CPT and ARES-RL. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A representative ARES-generated instance from the Medicine & Health domain. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Rubric-based rewards offer a promising way to extend reinforcement learning (RL) for large language models beyond tasks with automatically verifiable answers. However, scaling rubric-based RL remains challenging: existing approaches often rely on expert-written rubrics and manually constructed question sets, while fixed task-level rubrics may fail to capture the evaluation requirements of individual questions. We propose ARES (Automated Rubric synthEsis for Scalable RL), a framework for automatically constructing rubric-based RL data at scale. Starting from raw pretraining documents, ARES converts source knowledge into self-contained question-answer pairs and co-generates question-specific weighted rubrics, enabling instance-level reward supervision for open-ended responses. To improve diversity and quality, ARES conditions generation on domain labels and persona information, and applies validation filters for question self-containment, answer faithfulness, and rubric validity. Using ARES, we construct 100K rubric-annotated instances across ten domains. Experiments on seven benchmarks show that rubric-based RL trained with ARES, outperforms continual pretraining, supervised fine-tuning, and binary-reward RL, with the largest gains on multi-dimensional open-ended tasks such as healthcare and instruction following.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces ARES, a framework that automatically converts raw pretraining documents into self-contained question-answer pairs and co-generates question-specific weighted rubrics (conditioned on domain and persona labels), applies validation filters for self-containment/faithfulness/validity, and constructs 100K rubric-annotated instances across ten domains. Experiments on seven benchmarks are claimed to show that rubric-based RL using ARES outperforms continual pretraining, supervised fine-tuning, and binary-reward RL, with the largest gains on multi-dimensional open-ended tasks such as healthcare and instruction following.

Significance. If the results hold without circularity in the reward signals, the work could meaningfully advance scalable rubric-based RL for open-ended LLM tasks by removing the need for expert-written rubrics and manual question sets. The automated construction at 100K scale is a potential strength for reproducibility if the generation and filtering pipeline is shown to be robust.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments section: the central claim of outperformance on seven benchmarks is stated without any quantitative results, error bars, baseline details, ablation studies on the validation filters, or tables of per-task metrics, preventing verification of the reported gains (especially the largest gains on healthcare and instruction following).
  2. [Method] Method section (rubric co-generation and validation filters): because questions, answers, weighted rubrics, and the automated validators are all produced by the same generative process conditioned on domain/persona labels, the RL stage risks reinforcing stylistic patterns already present in the synthetic data rather than learning externally valid multi-dimensional criteria; no independent human validation or inter-rater agreement metrics are described to break this loop.
minor comments (1)
  1. [Abstract] The abstract would benefit from at least one concrete performance delta or table reference to support the outperformance claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback. We address each major comment below and describe the revisions we will make to improve clarity and address methodological concerns.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: the central claim of outperformance on seven benchmarks is stated without any quantitative results, error bars, baseline details, ablation studies on the validation filters, or tables of per-task metrics, preventing verification of the reported gains (especially the largest gains on healthcare and instruction following).

    Authors: We agree that the abstract and experiments section lack sufficient quantitative detail for verification. In the revised manuscript, we will update the abstract to report specific performance improvements with error bars and baseline comparisons. We will also expand the experiments section to include full per-task metric tables across all seven benchmarks, ablation results on the validation filters, and detailed baseline descriptions, with particular emphasis on the gains observed for healthcare and instruction-following tasks. revision: yes

  2. Referee: [Method] Method section (rubric co-generation and validation filters): because questions, answers, weighted rubrics, and the automated validators are all produced by the same generative process conditioned on domain/persona labels, the RL stage risks reinforcing stylistic patterns already present in the synthetic data rather than learning externally valid multi-dimensional criteria; no independent human validation or inter-rater agreement metrics are described to break this loop.

    Authors: We acknowledge the risk of circularity when generation, rubric creation, and validation all stem from the same model. The validation filters are implemented as post-hoc checks for self-containment, faithfulness, and rubric validity, but we agree that independent human assessment is needed to confirm external validity. In the revision, we will add a human evaluation study on a representative subset of the generated instances, reporting inter-rater agreement metrics to provide external validation of the rubric quality. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on benchmark comparisons

full rationale

The paper presents an empirical pipeline for generating rubric-annotated QA data from raw documents, followed by RL training and evaluation on seven external benchmarks. No equations, parameter fits, or derivation steps are described that reduce to the generation process by construction. Central performance claims are supported by direct comparisons against continual pretraining, SFT, and binary-reward baselines rather than self-referential definitions or load-bearing self-citations. This is a standard empirical contribution with no circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no equations, parameters, or explicit assumptions are stated beyond the implicit claim that generated rubrics are valid after filtering.

pith-pipeline@v0.9.0 · 5756 in / 1127 out tokens · 17770 ms · 2026-05-25T04:29:55.762168+00:00 · methodology

discussion (0)

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    thought":

    A Limitations Due to computational resource constraints, our experiments are conducted exclusively on Qwen3-4B- Base. This limits the scope of our findings. We are unable to validate whether ARES scales to larger models (e.g., 70B parameters), where rubric-based rewards may yield different dynamics due to increased model capacity and stronger baseline rea...

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